Sum-Product Based Optimization for Scalable Video Streams in Peer-to-Peer Mesh Network

Receiver heterogeneity of a P2P network can be effectively addressed by scalable video streams. Due to the discontinuous nature of scalable video, traditional convex-optimization approach is not applicable. We propose a message-passing based approach for optimization using the sum- product update algorithm. Advantage of this simple but elegant approach over other heuristic-based algorithm is that the optimization algorithm itself is independent of the underlying constraints. The algorithm iteratively updates layer allocation decision based on a given set of codewords. The codewords are binary representation of various network and video constraints. Therefore, any number of constraints can be used to generate a set of codewords without modifying the algorithm. To the best of our knowledge, this is the first work that systematically addresses the scalable video optimization problem. Preliminary simulation with up to 8 layers shows that the sum- product update process achieves an average layer delivery of 95% or higher.